Treatment and control groups

Combinatorics

Surgery

Pathology

Causal inference

Nursing

Health care

Economics

Randomized controlled trial

Intensive care medicine

Propensity score matching

Comparability

Health technology

Observational study

Psychological intervention

Population

Mathematics

Selection bias

Economic growth

Medicine

Confounding

Average treatment effect

Environmental health

Frontiers in Pharmacology

Randomized clinical trials (RCTs) are considered the gold-standard approach to estimate effects of treatment on outcomes. They are also the designs of choice for health technology assessment (HTA). Randomization ensures comparability, in both measured and unmeasured pre-treatment characteristics, of patients assigned to treatment and control or comparator. However, even adequately powered RCTs are not always feasible for reasons such as cost, time, ethical, and practical constraints. RCTs rely on data collected on selected, homogeneous population under highly controlled conditions; hence, they provide evidence on efficacy of interventions rather than on effectiveness. Alternatively, observational studies can can provide evidence on the relative effectiveness or safety of a health technology compared to one or more alternatives when provided under the routine setting of health care practice. In observational studies, however, treatment assignment is a non-random process hence treatment groups may not be comparable in their pre-treatment characteristics. As a result, direct comparison of outcomes between treatment groups lead to biased estimate of treatment effect. Propensity score methods have been used to achieve comparability of treatment groups in terms of their measured pre-treatment covariates and thereby controlling for confounding bias in estimating treatment effects. Despite the popularity of propensity scores methods and recent important advances, misunderstandings on their applications and limitations are too common. In this article, we provide a review of the methods, extended applications, recent advances, strengths and limitations.

Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances